# Demonstrating research subcommunities in mathematical networks

## TL;DR

This paper introduces a visualization method for identifying and tracking research subcommunities within small scientific networks, using clustering and graph layout algorithms, exemplified on the GEAR mathematical network.

## Contribution

It presents a novel approach combining clustering and visualization tools to analyze and display research subcommunities in scientific networks, including temporal evolution tracking.

## Key findings

- Effective visualization of research subcommunities
- Tools for tracking community evolution over time
- Web app implementation for interactive exploration

## Abstract

We propose a method for demonstrating sub community structure in scientific networks of relatively small size from analyzing databases of publications. Research relationships between the network members can be visualized as a graph with vertices corresponding to authors and with edges indicating joint authorship. Using a fast clustering algorithm combined with a graph layout algorithm, we demonstrate how to display these clustering results in an attractive and informative way. The small size of the graph allows us to develop tools that keep track of how these research sub communities evolve in time, as well as to present the research articles that create the links between the network members. These tools are included in a web app, where the visitor can easily identify the various sub communities, providing also valuable information for administrational purposes. Our method was developed for the GEAR mathematical network and it can be applied to other networks.